=Paper=
{{Paper
|id=Vol-1353/paper_01
|storemode=property
|title=GAIL: A Genetics Argumentation Inquiry Learning System
|pdfUrl=https://ceur-ws.org/Vol-1353/paper_01.pdf
|volume=Vol-1353
|dblpUrl=https://dblp.org/rec/conf/maics/GreenHMNT15
}}
==GAIL: A Genetics Argumentation Inquiry Learning System==
https://ceur-ws.org/Vol-1353/paper_01.pdf
GAIL: A Genetics Argumentation Inquiry Learning System
Nancy L. Green, Mark Hinshaw, Carl Martensen, Meghana Narasimhan and Tshering Tobgay
Department of Computer Science
University of North Carolina Greensboro
Greensboro, NC 27402 USA
nlgreen@uncg.edu
Abstract Each GAIL lesson requires learners to construct
This paper discusses ongoing work to build an arguments for and against certain hypotheses about a
argumentation inquiry learning system, GAIL. The purpose genetics case, e.g., about an infant who may have an
of GAIL is to support students in constructing scientific inherited metabolic disorder or someone who inherited a
arguments in an undergraduate genetics course in order to genetic variant that is associated with increased risk of
facilitate deeper learning and improve argumentation skill. colon cancer. A prototype user interface is shown in
Students can construct argument diagrams using a drag- Figure 1 (see last page). Information relevant to the lesson
and-drop graphical user interface. The system constructs
arguments on-the-fly to use as a knowledge source for
is provided by GAIL on the left-hand side of the screen:
evaluating the learners’ arguments and providing the Problem (to give a certain argument); Hypotheses
intelligent feedback. (which can be used in the argument, but note that some
are incorrect); Data from medical records about the
patient and the patient’s biological family; and
Introduction Connections, a list of facts or principles of genetics. The
center of the screen shows two arguments constructed by a
Argumentation plays an important role in science. There learner. To construct the arguments, the learner searched
has been significant interest within the field of science for text components on the left-hand side of the screen,
education in argumentation. However, students’ dragged them into the workspace in the center of the
arguments have been shown to be deficient in a number of screen, and connected the components. Arrows point from
ways, e.g., lacking support for claims (Bell and Linn support to conclusion. The connection between support
2000; Jiménez-Aleixandre, Rodríguez and Duschl 2000), and conclusion – known as the warrant in argumentation
failing to provide alternative explanations (Lawson 2003; theory (Toulmin 1998) – is linked by a line to the arrow.
Schwarz et al. 2003), and using inaccurate or irrelevant In Figure 1, the problem is to give two arguments for
support (Zohar and Nemet 2002). Computer-supported the hypothesis that the patient (referred to as J.B.) has
cooperative learning systems for argumentation have been cystic fibrosis, i.e., has two variant alleles of the CFTR
developed (Kirschner et al. 2003; Scheuer et al. 2010; gene. The leftmost “chain” of arguments begins with data
Pinkwart and McLaren 2012) but they do not provide (at the bottom of the argument diagram) about J.B.’s
human-level expertise in evaluating student respiratory problems. The learner used that data to support
argumentation. Furthermore, in larger-enrollment classes an intermediate hypothesis that J.B. has thickened mucus
human teachers may not have sufficient time to evaluate in the lungs, which is used to support an intermediate
learners’ arguments nor to provide one-on-one feedback hypothesis that J.B. has abnormal CFTR protein, which is
as the learner works on an argumentation lesson. used to support the main hypothesis/conclusion that J.B.
To address this problem we are implementing a has cystic fibrosis. Branching from the right hand side of
prototype genetics argumentation inquiry learning system, the diagram, connections (warrants) provide justification
GAIL. GAIL will support learning to argue about cases in for each step of the argument. The second argument for
human genetics. This is a field that applies findings from the same hypothesis begins with data about J.B.’s lab test
genetics research to biomedical reasoning. GAIL is result.
designed for use in an introductory genetics course for GAIL’s innovation is that the system can generate
undergraduates that many biology majors find the most arguments for evaluating the correctness of the learner’s
challenging course in the biology core curriculum. We arguments, rather than requiring the arguments to be
hope that use of GAIL will improve argumentation skill, constructed by a teacher. Use of the generated arguments
facilitate deeper learning of genetics, and increase interest enables GAIL to provide intelligent feedback on both the
and engagement in science. structure and content of the learner’s argument.
condition predisposing one to colon cancer) based upon
System Design the data that genetic testing showed a variant MLH1 allele,
The author of an argumentation lesson to be used in and the connection (warrant) that having HNPCC
GAIL creates an XML-formatted file that contains: (1) typically leads to that test result. (The exception condition
strings of natural language text -- the problem, hypotheses, for this argumentation scheme asks whether there is an
data, and connections -- to be displayed to learners on the alternative explanation for the data.) An argument
left-hand side of the graphical user interface as shown in diagram representing this argument is shown in Figure 3;
Figure 1; (2) a specification of an internal causal domain to save space, instead of natural language text from the
model; and (3) mapping of the natural language strings in graphical user interface, the diagram uses letters
(1) to concepts and relations in the domain model. GAIL’s representing propositions, where A is the conclusion, B is
the data, and S+(A,B) is the warrant.
Authoring Tool provides (1) to the user interface, uses
(2) to build an internal Knowledge Base, and stores (3) to
enable GAIL’s Argument Evaluator to semantically A
interpret learners’ argument diagrams, to avoid the
challenge of interpreting natural language input.
Based on our previous work on modeling genetics S+(A,B)
(Green 2005), a Knowledge Base (KB) describes (i)
instances of small set of concepts in human genetics (e.g. B
genotype, protein, phenotype) and (ii) causal relations
between these concepts. Causal relations are defined in
Figure 3. Simple argument.
terms of influence and synergy relations of a qualitative
probabilistic network (QPN) (Druzdzel and Henrion
Figure 4 shows a more complicated argument. The
1993). Different genetics KBs can be constructed
main claim (A=1) is that a patient’s mother has exactly
automatically from XML-language descriptions of the
one mutated CFTR allele. The left-hand subargument is
causal model specified using the Authoring Tool.
for the hypothesis that she has one or two mutated CFTR
Argumentation schemes are descriptions of acceptable,
alleles. That subargument is supported by the hypothesis
but often defeasible, patterns of reasoning (Walton, Reed
(D=2) that the patient has cystic fibrosis (two mutated
and Macagno 2008). Following the same approach as in
CFTR alleles), and is warranted by the synergy relation,
our previous research on argument generation (Green,
X0(, D=2), i.e., that a child who has two
Dwight, Navoraphan and Stadler 2011), GAIL’s
mutated alleles inherited one from the mother and one
Argument Generator creates arguments by instantiating
from the father. Note that the claim D=2 would be
abstract argumentation schemes with concepts and
supported by another subargument (not shown in Figure
relations from a QPN. The argumentation schemes are
4). The right-hand subargument is for the hypothesis that
formalized in structures including claim/conclusion, data,
the mother does not have two mutated CFTR alleles. This
and warrant. The propositions used as claim or data
is supported by the data (¬C) that she does not have cystic
describe states of variables in a QPN. The warrant
fibrosis symptoms, and warranted by the positive
expresses formal constraints on the nodes of the QPN in
influence relation between having two mutated CFTR
terms of probabilistic influence and synergy relations. The
alleles and symptoms of cystic fibrosis.
distinction between premises as data and warrant reflects
their difference in function and source of information.
A=1
Data premises refer to a particular case, whereas warrants
describe biomedical principles and other generalized
knowledge. The condition of GAIL’s argumentation
schemes is used to represent possible exceptions. &
For example, the argumentation scheme for reasoning
from effect to cause, shown in Figure 2, can be
instantiated from a KB to create an argument that a patient A=1 or A=2 A≠2
has HNPCC (a mutation in the MLH1 gene, a hereditary
X0(,
Claim: A ≥ a S+(A=2,C)
D=2)
Data: B ≥ b D=2
Warrant: S+(, ) ¬C
Condition: ¬ exists C X-({C,A},): C ≥ c
Figure 2. Argumentation scheme. Figure 4. More complicated argument.
Currently, GAIL employs seven argumentation argument. After each try, the Feedback Generator selects
schemes. Arguments such as that shown in Figure 4 can be the most general (lowest level) unused message for an
generated by chaining and/or conjoining subarguments. error; each time the student makes the same error on a
Arguments are represented internally as directed acyclic subsequent try, the next more specific (next higher level)
graphs. Unlike previous educational systems in which all message is selected. A positive message is generated when
possible arguments to be used by the system had to be an error is corrected on the next try. Currently, the
encoded by an author in natural language (e.g. Woolf et Feedback Generator displays only the message for the
al. 2005) or in propositional logic (e.g. Yuan et al. 2008), most serious error to the student, but writes all of the
GAIL’s arguments are generated by the system on-the-fly. detected errors to a logfile for inspection by the teacher.
Since the argument generator and schemes do not encode
domain-specific or patient-specific content, they can be
used to generate arguments in any domain whose domain Feedback Example
knowledge can be represented in a similar format.
To illustrate the learner’s interaction with GAIL,
After a learner has created an argument diagram,
suppose the problem was to give an argument for the
GAIL’s Argument Evaluator’s task is to evaluate the
hypothesis that J.B.’s brother might have malnutrition and
acceptability of the structure and content of the learners’
poor growth. Internally, GAIL generates a chained
argument diagram. First, the learner’s diagram is
argument beginning with the data that J.B.’s brother has
translated into an argument structure containing KB
been diagnosed as having cystic fibrosis, which supports
concepts and links. The translation process uses the
an intermediate hypothesis that his CFTR protein is
correspondences between text the learner sees on the
abnormal, which supports an intermediate hypothesis that
screen and KB concepts and mappings provided via the
he might have pancreatic abnormality, which supports the
Authoring Tool from (3). The translated structure is in the
main hypothesis that he might have malnutrition and poor
same representation as arguments produced automatically
growth. (The warrants of GAIL’s argument are not
by the Argument Generator. Then the internal
described here to save space.) However, on the first try
representation of the learner’s argument is compared to all
the learner’s argument contains the main claim that J.B.
possible arguments created for the given problem by the
(rather than J.B.’s brother) has cystic fibrosis, which does
Argument Generator.
not match the problem. Since this type of error has been
GAIL’s Feedback Generator can respond to the given the highest severity code, GAIL would tell the
following types of errors, where components of the student that the main claim of his argument does not
learner’s argument are enclosed in brackets below: match the problem. On the second try, the student fixes
• does not match the claim to be the main claim and constructs a new argument. GAIL
argued for in the problem. informs him that the problem noted on the last try has
• is unsupported (i.e. no argument is been fixed. However, the student’s argument is missing
provided for it). the intermediate hypothesis that J.B.’s brother might have
• does not support the given pancreatic abnormality, so GAIL also informs the student
. that one or more intermediate hypotheses are missing
• does not support the given between J.B.’s brother having abnormal CFTR protein
directly; one or more hypotheses and J.B.’s brother having malnutrition and poor growth.
are missing between it and the given On the third try, the student adds the missing hypothesis
. but provides an irrelevant warrant. GAIL would inform
• Additional data or hypothesis must be conjoined the student that he has made progress but that the warrant
to the given . he just added is irrelevant to that subargument.
• is given as supporting
but it should be conjoined
to . Conclusion
• The warrant is missing between the given This paper discusses ongoing work to build an
and . argumentation inquiry learning system, GAIL. The
• The given is irrelevant to the given purpose of GAIL is to support students in constructing
and . scientific arguments in an undergraduate genetics course
Note that, unique to GAIL, most of the above types of in order to facilitate deeper learning and improve
errors are semantic in nature. argumentation skill. The system generates arguments on-
For each type of error, the author of a GAIL lesson or a the-fly to use as a knowledge source for evaluating the
system developer can provide a severity code and three learners’ arguments and providing formative and
levels of feedback message templates in an XML- summative intelligent feedback.
formatted file. In the current implementation of GAIL, a All of the components described in this paper have
student is allowed three tries to construct an acceptable been implemented in Java. Future work includes
improvements to the user interface and the Feedback
Generator. The Feedback Generator will be made more Kirschner, P.A., Buckingham Shum, S.J., and Carr, C.S.
intelligent to address certain types of errors that we have (Eds.) 2003. Visualizing Argumentation. London, UK:
observed in our formative evaluation studies. For Springer.
example, a learner “flattened” a chained argument into a
one-level structure by conjoining together all of the data Lawson, A. 2003. The Nature and Development of
and intermediate hypotheses. Note that in this case, the Hypothetico-Predictive Argumentation with Implications
learner has selected the correct content but has just not for Science Teaching. International Journal of Science
structured the argument properly into subarguments and Education 25(11): 1387-1408.
shown how one subargument builds upon another
subargument. Because the Feedback Generator has access Pinkwart, N. and McLaren, B.M. (Eds.) 2012. Educa-
to arguments constructed by GAIL’s Argument Generator, tional Technologies for Teaching Argumentation Skills.
the Feedback Generator will be able to detect this type of Sharjah: Bentham Science Publishers
error and provide more meaningful feedback than systems
that do not have access to content. After these
Scheuer, O., Loll, F., Pinkwart, N., and McLaren, B.M.
improvements are made, we plan to evaluate GAIL’s
2010. Computer-Supported Argumentation: A Review of
effectiveness in an undergraduate genetics course.
the State of the Art. Computer-Supported Collaborative
Learning 5(1): 43-102.
Acknowledgments
Schwarz, B., Neuman, Y., Gil, J., and Ilya, M. 2003.
Former graduate students Mark Hinshaw, Carl Martensen, Construction of Collective and Individual Knowledge in
Meghana Narasimhan, and Tshering Tobgay contributed Argumentative Activity. Journal of the Learning Sciences
to the implementation of GAIL for their MS Projects. 12(2): 219-256.
Former graduate students Benjamin Wyatt and Chris Cain
also contributed to the implementation of GAIL. Wyatt Toulmin, S.E. 1998. The Uses of Argument, Cambridge,
and Martensen received support from a UNCG Regular UK: Cambridge University Press.
Faculty grant and Cain received support from the
Computer Science Department. Dr. Malcolm Schug of the Walton, D., Reed, C., and Macagno, F. 2008.
UNCG Department of Biology has provided helpful Argumentation Schemes. Cambridge, UK: Cambridge
feedback on the project. University Press.
Woolf, B., Reid, J., Stillings, N., Bruno, M., Murray, D.,
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Figure 1. Screen shot of prototype GAIL user interface